import sys
import torch
import torch._C as _C
from collections import OrderedDict
import torch.utils.hooks as hooks
import warnings
import weakref
from torch._six import imap
from torch._C import _add_docstr
from numbers import Number
# NB: If you subclass Tensor, and want to share the subclassed class
# across processes, you must also update torch/multiprocessing/reductions.py
# to define a ForkingPickler serialization mode for the class.
#
# NB: If you add a new method to Tensor, you must update
# torch/__init__.py.in to add a type annotation for your method;
# otherwise, it will not show up in autocomplete.
class Tensor(torch._C._TensorBase):
def __deepcopy__(self, memo):
if not self.is_leaf:
raise RuntimeError("Only Tensors created explicitly by the user "
"(graph leaves) support the deepcopy protocol at the moment")
if id(self) in memo:
return memo[id(self)]
with torch.no_grad():
if self.is_sparse:
new_tensor = self.clone()
else:
new_storage = self.storage().__deepcopy__(memo)
new_tensor = self.new()
new_tensor.set_(new_storage, self.storage_offset(), self.size(), self.stride())
memo[id(self)] = new_tensor
new_tensor.requires_grad = self.requires_grad
return new_tensor
def __reduce_ex__(self, proto):
# See Note [Don't serialize hooks]
torch.utils.hooks.warn_if_has_hooks(self)
if self.is_quantized:
args = (self.storage(),
self.storage_offset(),
tuple(self.size()),
self.stride(),
self.q_scale(),
self.q_zero_point(),
self.requires_grad,
OrderedDict()) # TODO: self.qscheme()
return (torch._utils._rebuild_qtensor, args)
else:
args = (self.storage(),
self.storage_offset(),
tuple(self.size()),
self.stride(),
self.requires_grad,
OrderedDict()) # previously was self._backward_hooks
return (torch._utils._rebuild_tensor_v2, args)
def __setstate__(self, state):
# Warning: this method is NOT called when you torch.load() a tensor;
# that is managed by _rebuild_tensor_v2
if not self.is_leaf:
raise RuntimeError('__setstate__ can be only called on leaf Tensors')
if len(state) == 4:
# legacy serialization of Tensor
self.set_(*state)
return
elif len(state) == 5:
# legacy serialization of Variable
self.data = state[0]
state = (state[3], state[4], state[2])
# The setting of _backward_hooks is expected to be a no-op.
# See Note [Don't serialize hooks]
self.requires_grad, _, self._backward_hooks = state
def __repr__(self):
# All strings are unicode in Python 3, while we have to encode unicode
# strings in Python2. If we can't, let python decide the best
# characters to replace unicode characters with.
if sys.version_info > (3,):
return torch._tensor_str._str(self)
else:
if hasattr(sys.stdout, 'encoding'):
return torch._tensor_str._str(self).encode(
sys.stdout.encoding or 'UTF-8', 'replace')
else:
return torch._tensor_str._str(self).encode('UTF-8', 'replace')
[docs] def backward(self, gradient=None, retain_graph=None, create_graph=False):
r"""Computes the gradient of current tensor w.r.t. graph leaves.
The graph is differentiated using the chain rule. If the tensor is
non-scalar (i.e. its data has more than one element) and requires
gradient, the function additionally requires specifying ``gradient``.
It should be a tensor of matching type and location, that contains
the gradient of the differentiated function w.r.t. ``self``.
This function accumulates gradients in the leaves - you might need to
zero them before calling it.
Arguments:
gradient (Tensor or None): Gradient w.r.t. the
tensor. If it is a tensor, it will be automatically converted
to a Tensor that does not require grad unless ``create_graph`` is True.
None values can be specified for scalar Tensors or ones that
don't require grad. If a None value would be acceptable then
this argument is optional.
retain_graph (bool, optional): If ``False``, the graph used to compute
the grads will be freed. Note that in nearly all cases setting
this option to True is not needed and often can be worked around
in a much more efficient way. Defaults to the value of
``create_graph``.
create_graph (bool, optional): If ``True``, graph of the derivative will
be constructed, allowing to compute higher order derivative
products. Defaults to ``False``.
"""
torch.autograd.backward(self, gradient, retain_graph, create_graph)
[docs] def register_hook(self, hook):
r"""Registers a backward hook.
The hook will be called every time a gradient with respect to the
Tensor is computed. The hook should have the following signature::
hook(grad) -> Tensor or None
The hook should not modify its argument, but it can optionally return
a new gradient which will be used in place of :attr:`grad`.
This function returns a handle with a method ``handle.remove()``
that removes the hook from the module.
Example::
>>> v = torch.tensor([0., 0., 0.], requires_grad=True)
>>> h = v.register_hook(lambda grad: grad * 2) # double the gradient
>>> v.backward(torch.tensor([1., 2., 3.]))
>>> v.grad
2
4
6
[torch.FloatTensor of size (3,)]
>>> h.remove() # removes the hook
"""
if not self.requires_grad:
raise RuntimeError("cannot register a hook on a tensor that "
"doesn't require gradient")
if self._backward_hooks is None:
self._backward_hooks = OrderedDict()
if self.grad_fn is not None:
self.grad_fn._register_hook_dict(self)
handle = hooks.RemovableHandle(self._backward_hooks)
self._backward_hooks[handle.id] = hook
return handle
def reinforce(self, reward):
def trim(str):
return '\n'.join([line.strip() for line in str.split('\n')])
raise RuntimeError(trim(r"""reinforce() was removed.
Use torch.distributions instead.
See https://pytorch.org/docs/master/distributions.html
Instead of:
probs = policy_network(state)
action = probs.multinomial()
next_state, reward = env.step(action)
action.reinforce(reward)
action.backward()
Use:
probs = policy_network(state)
# NOTE: categorical is equivalent to what used to be called multinomial
m = torch.distributions.Categorical(probs)
action = m.sample()
next_state, reward = env.step(action)
loss = -m.log_prob(action) * reward
loss.backward()
"""))
detach = _add_docstr(_C._TensorBase.detach, r"""
Returns a new Tensor, detached from the current graph.
The result will never require gradient.
.. note::
Returned Tensor shares the same storage with the original one.
In-place modifications on either of them will be seen, and may trigger
errors in correctness checks.
IMPORTANT NOTE: Previously, in-place size / stride / storage changes
(such as `resize_` / `resize_as_` / `set_` / `transpose_`) to the returned tensor
also update the original tensor. Now, these in-place changes will not update the
original tensor anymore, and will instead trigger an error.
For sparse tensors:
In-place indices / values changes (such as `zero_` / `copy_` / `add_`) to the
returned tensor will not update the original tensor anymore, and will instead
trigger an error.
""")
detach_ = _add_docstr(_C._TensorBase.detach_, r"""
Detaches the Tensor from the graph that created it, making it a leaf.
Views cannot be detached in-place.
""")
[docs] def retain_grad(self):
r"""Enables .grad attribute for non-leaf Tensors."""
if self.grad_fn is None: # no-op for leaves
return
if not self.requires_grad:
raise RuntimeError("can't retain_grad on Tensor that has requires_grad=False")
if hasattr(self, 'retains_grad'):
return
weak_self = weakref.ref(self)
def retain_grad_hook(grad):
var = weak_self()
if var is None:
return
if var._grad is None:
var._grad = grad.clone()
else:
var._grad = var._grad + grad
self.register_hook(retain_grad_hook)
self.retains_grad = True
[docs] def is_pinned(self):
r"""Returns true if this tensor resides in pinned memory"""
storage = self.storage()
return storage.is_pinned() if storage else False
[docs] def is_shared(self):
r"""Checks if tensor is in shared memory.
This is always ``True`` for CUDA tensors.
"""
return self.storage().is_shared()
[docs] def share_memory_(self):
r"""Moves the underlying storage to shared memory.
This is a no-op if the underlying storage is already in shared memory
and for CUDA tensors. Tensors in shared memory cannot be resized.
"""
self.storage().share_memory_()
return self
def __reversed__(self):
r"""Reverses the tensor along dimension 0."""
if self.dim() == 0:
return self
else:
return self.flip(0)
[docs] def norm(self, p="fro", dim=None, keepdim=False, dtype=None):
r"""See :func:`torch.norm`"""
return torch.norm(self, p, dim, keepdim, dtype=dtype)
[docs] def lu(self, pivot=True, get_infos=False):
r"""See :func:`torch.lu`"""
# If get_infos is True, then we don't need to check for errors and vice versa
LU, pivots, infos = torch._lu_with_info(self, pivot=pivot, check_errors=(not get_infos))
if get_infos:
return LU, pivots, infos
else:
return LU, pivots
[docs] def gels(self, A):
r"""See :func:`torch.lstsq`"""
warnings.warn("torch.gels is deprecated in favour of torch.lstsq and will be "
"removed in the next release. Please use torch.lstsq instead.", stacklevel=2)
return super(Tensor, self).lstsq(A)
[docs] def stft(self, n_fft, hop_length=None, win_length=None, window=None,
center=True, pad_mode='reflect', normalized=False, onesided=True):
r"""See :func:`torch.stft`
.. warning::
This function changed signature at version 0.4.1. Calling with
the previous signature may cause error or return incorrect result.
"""
return torch.stft(self, n_fft, hop_length, win_length, window, center,
pad_mode, normalized, onesided)
def resize(self, *sizes):
warnings.warn("non-inplace resize is deprecated")
from torch.autograd._functions import Resize
return Resize.apply(self, sizes)
def resize_as(self, tensor):
warnings.warn("non-inplace resize_as is deprecated")
from torch.autograd._functions import Resize
return Resize.apply(self, tensor.size())
[docs] def split(self, split_size, dim=0):
r"""See :func:`torch.split`
"""
if isinstance(split_size, int):
return super(Tensor, self).split(split_size, dim)
else:
return super(Tensor, self).split_with_sizes(split_size, dim)
[docs] def unique(self, sorted=True, return_inverse=False, return_counts=False, dim=None):
r"""Returns the unique elements of the input tensor.
See :func:`torch.unique`
"""
return torch.unique(self, sorted=sorted, return_inverse=return_inverse, return_counts=return_counts, dim=dim)
[docs] def unique_consecutive(self, return_inverse=False, return_counts=False, dim=None):
r"""Eliminates all but the first element from every consecutive group of equivalent elements.
See :func:`torch.unique_consecutive`
"""
return torch.unique_consecutive(self, return_inverse=return_inverse, return_counts=return_counts, dim=dim)
def __rsub__(self, other):
return _C._VariableFunctions.rsub(self, other)
def __rdiv__(self, other):
if self.dtype.is_floating_point:
return self.reciprocal() * other
else:
return (self.double().reciprocal() * other).type_as(self)
__rtruediv__ = __rdiv__
__itruediv__ = _C._TensorBase.__idiv__
__pow__ = _C._TensorBase.pow
def __format__(self, format_spec):
if self.dim() == 0:
return self.item().__format__(format_spec)
return object.__format__(self, format_spec)
def __ipow__(self, other):
raise NotImplementedError("in-place pow not implemented")
def __rpow__(self, other):
return self.new_tensor(other) ** self
def __floordiv__(self, other):
result = self / other
if result.dtype.is_floating_point:
result = result.trunc()
return result
def __rfloordiv__(self, other):
result = other / self
if result.dtype.is_floating_point:
result = result.trunc()
return result
__neg__ = _C._TensorBase.neg
__eq__ = _C._TensorBase.eq
__ne__ = _C._TensorBase.ne
__lt__ = _C._TensorBase.lt
__le__ = _C._TensorBase.le
__gt__ = _C._TensorBase.gt
__ge__ = _C._TensorBase.ge
__abs__ = _C._TensorBase.abs
def __len__(self):
if self.dim() == 0:
raise TypeError("len() of a 0-d tensor")
return self.shape[0]
def __iter__(self):
# NB: we use 'imap' and not 'map' here, so that in Python 2 we get a
# generator and don't eagerly perform all the indexes. This could
# save us work, and also helps keep trace ordering deterministic
# (e.g., if you zip(*hiddens), the eager map will force all the
# indexes of hiddens[0] before hiddens[1], while the generator
# map will interleave them.)
if self.dim() == 0:
raise TypeError('iteration over a 0-d tensor')
if torch._C._get_tracing_state():
warnings.warn('Iterating over a tensor might cause the trace to be incorrect. '
'Passing a tensor of different shape won\'t change the number of '
'iterations executed (and might lead to errors or silently give '
'incorrect results).', category=RuntimeWarning)
return iter(imap(lambda i: self[i], range(self.size(0))))
def __hash__(self):
return id(self)
def __dir__(self):
tensor_methods = dir(self.__class__)
tensor_methods.remove('volatile') # deprecated
attrs = list(self.__dict__.keys())
keys = tensor_methods + attrs
# property only available dense, cuda tensors
if (not self.is_cuda) or self.is_sparse:
keys.remove("__cuda_array_interface__")
return sorted(keys)
# Numpy array interface, to support `numpy.asarray(tensor) -> ndarray`
__array_priority__ = 1000 # prefer Tensor ops over numpy ones
def __array__(self, dtype=None):
if dtype is None:
return self.numpy()
else:
return self.numpy().astype(dtype, copy=False)
# Wrap Numpy array again in a suitable tensor when done, to support e.g.
# `numpy.sin(tensor) -> tensor` or `numpy.greater(tensor, 0) -> ByteTensor`
def __array_wrap__(self, array):
if array.dtype == bool:
# Workaround, torch has no built-in bool tensor
array = array.astype('uint8')
return torch.from_numpy(array)
def __contains__(self, element):
r"""Check if `element` is present in tensor
Arguments:
element (Tensor or scalar): element to be checked
for presence in current tensor"
"""
if isinstance(element, (torch.Tensor, Number)):
return (element == self).any().item()
return NotImplemented
@property
def __cuda_array_interface__(self):
"""Array view description for cuda tensors.
See:
https://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html
"""
# raise AttributeError for unsupported tensors, so that
# hasattr(cpu_tensor, "__cuda_array_interface__") is False.
if not self.is_cuda:
raise AttributeError(
"Can't get __cuda_array_interface__ on non-CUDA tensor type: %s "
"If CUDA data is required use tensor.cuda() to copy tensor to device memory." %
self.type()
)
if self.is_sparse:
raise AttributeError(
"Can't get __cuda_array_interface__ on sparse type: %s "
"Use Tensor.to_dense() to convert to a dense tensor first." %
self.type()
)
# RuntimeError, matching tensor.__array__() behavior.
if self.requires_grad:
raise RuntimeError(
"Can't get __cuda_array_interface__ on Variable that requires grad. "
"If gradients aren't required, use var.detach() to get Variable that doesn't require grad."
)
# CUDA devices are little-endian and tensors are stored in native byte
# order. 1-byte entries are endian-agnostic.
typestr = {
torch.float16: "<f2",
torch.float32: "<f4",
torch.float64: "<f8",
torch.uint8: "|u1",
torch.int8: "|i1",
torch.int16: "<i2",
torch.int32: "<i4",
torch.int64: "<i8",
}[self.dtype]
itemsize = self.storage().element_size()
shape = self.shape
strides = tuple(s * itemsize for s in self.stride())
data = (self.data_ptr(), False) # read-only is false
return dict(typestr=typestr, shape=shape, strides=strides, data=data, version=0)
__module__ = 'torch'
